GE Healthcare has formed a strategic partnership with the National Association of Software and Services Companies (NASSCOM) to bring digital healthcare to India.
NASSCOM's Center of Excellence - Internet of Things (CoE-IoT) is considered India's deep-tech innovation hub for collaborative start-up entities in a market ready for substantial growth, as digital technology connects doctors and patients and fosters low-cost medical devices and technology-enabled diagnostics. GE and NASSCOM also plan to work with officials in the Indian government to shape policies around digital health.
"Digital solutions help drive access to better quality healthcare, significantly reduce cost of treatment, and improve quality of health outcomes. ... We need an ecosystem of partners with whom we can work to supplement the work already underway at our research centers," said Dileep Mangsuli, chief technology officer for GE Healthcare South Asia, in a statement. "This partnership with NASSCOM CoE-IoT will help us bring to market solutions that improve people's lives."
![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
